Search for a command to run...
Accurate characterization of soil properties is fundamental for quantifying terrestrial carbon stocks, land–atmosphere interactions, and assessing agroecosystem functioning and ecosystem services. Launched in August 2024, Tanager-1 is the first satellite in Planet’s hyperspectral constellation, delivering over 400+ contiguous spectral bands across the 400–2,500 nm range at a spatial resolution of 30 m. Such data is vital for monitoring vegetation and soil health. However, retrieving soil properties from satellite data in diverse agricultural landscapes remains challenging in heterogeneous croplands where soil, vegetation, and moisture vary strongly within and between fields. This is a particular issue in perennial cropping systems, such as vineyards. This research, conducted as part of the Horizon Europe AI4SoilHealth project, explores the potential of Tanager data to derive key soil properties, such as soil organic carbon (SOC) and soil texture, while addressing the confounding effects of different scenes' radiometric signatures.The methodology leverages the European Soil Data Center's (ESDAC) Land Use/Cover Area Frame Statistical Survey (LUCAS), which includes around 20,000 soil samples, as a foundation for retrieving soil properties from Tanager surface reflectance, using machine learning approaches such as partial least squares regression (PLSR). The study investigates strategies for decoupling the soil and vegetation components. For example, drawing on the PROSAIL radiative transfer model to explicitly simulate and account for the vegetation contribution in the training dataset and using an autoencoder-based spectral unmixing model to mitigate vegetation effects. and estimate bare soil reflectance from Tanager observations in a pre-processing step. Validation is supported by field-based spectrometers and laboratory analysis of physical soil samples to provide the ground truth for distinct scene endmembers.Preliminary findings suggest that the high signal-to-noise ratio of the Tanager hyperspectral sensor, when combined with artificial intelligence models, shows promising improvements in soil property retrieval accuracy.